Explainable artificial intelligence for 6G: Improving trust between human and machine
W Guo - IEEE Communications Magazine, 2020 - ieeexplore.ieee.org
As 5G mobile networks are bringing about global societal benefits, the design phase for 6G
has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery …
has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Bayesian probabilistic numerical methods
Over forty years ago average-case error was proposed in the applied mathematics literature
as an alternative criterion with which to assess numerical methods. In contrast to worst-case …
as an alternative criterion with which to assess numerical methods. In contrast to worst-case …
Bayesian numerical homogenization
H Owhadi - Multiscale Modeling & Simulation, 2015 - SIAM
Numerical homogenization, ie, the finite-dimensional approximation of solution spaces of
PDEs with arbitrary rough coefficients, requires the identification of accurate basis elements …
PDEs with arbitrary rough coefficients, requires the identification of accurate basis elements …
Learning dynamical systems from data: a simple cross-validation perspective, part I: parametric kernel flows
Regressing the vector field of a dynamical system from a finite number of observed states is
a natural way to learn surrogate models for such systems. We present variants of cross …
a natural way to learn surrogate models for such systems. We present variants of cross …
Multigrid with rough coefficients and multiresolution operator decomposition from hierarchical information games
H Owhadi - Siam Review, 2017 - SIAM
We introduce a near-linear complexity (geometric and meshless/algebraic) multigrid/
multiresolution method for PDEs with rough (L^∞) coefficients with rigorous a priori …
multiresolution method for PDEs with rough (L^∞) coefficients with rigorous a priori …
Lessons on climate sensitivity from past climate changes
Over the last decade, our understanding of climate sensitivity has improved considerably.
The climate system shows variability on many timescales, is subject to non-stationary forcing …
The climate system shows variability on many timescales, is subject to non-stationary forcing …
A survey of online data-driven proactive 5G network optimisation using machine learning
In the fifth-generation (5G) mobile networks, proactive network optimisation plays an
important role in meeting the exponential traffic growth, more stringent service requirements …
important role in meeting the exponential traffic growth, more stringent service requirements …
Kernel flows: From learning kernels from data into the abyss
Learning can be seen as approximating an unknown function by interpolating the training
data. Although Kriging offers a solution to this problem, it requires the prior specification of a …
data. Although Kriging offers a solution to this problem, it requires the prior specification of a …
More data can expand the generalization gap between adversarially robust and standard models
Despite remarkable success in practice, modern machine learning models have been found
to be susceptible to adversarial attacks that make human-imperceptible perturbations to the …
to be susceptible to adversarial attacks that make human-imperceptible perturbations to the …